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15 - Confronting the Economic Model with the Data

Published online by Cambridge University Press:  02 December 2009

David Colander
Affiliation:
Middlebury College, Vermont
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Summary

INTRODUCTION

Econometrics is about confronting economic models with the data. In doing so it is crucial to choose a statistical model that not only contains the economic model as a submodel, but also contains the data generating process. When this is the case, the statistical model can be analyzed by likelihood methods. When this is not the case, but likelihood methods are applied nonetheless, the result is incorrect inference. In this chapter, we illustrate the problem of possible incorrect inference with a recent application of a DSGE model to U.S. data (Ireland, 2004). Specifically, this chapter discusses two broad methodological questions.

  • How should a statistical model be chosen to achieve valid inference for the economic model?

  • Given a correctly chosen statistical model, can we rely on the asymptotic results found in the statistical literature for the analysis of the data at hand?

Using some simple examples, the chapter first discusses some unfortunate consequences of applying Gaussian maximum likelihood when the chosen statistical model does not properly describe the data. It also demonstrates that even when the correct statistical model is chosen, asymptotic results derived for stationary processes cannot be used to conduct inference on the steady state value for a highly persistent stationary process.

When taking an economic model to the data in order to conduct inference “we need a stochastic formulation to make simplified relations elastic enough for applications,” to quote Haavelmo (1943).

Type
Chapter
Information
Post Walrasian Macroeconomics
Beyond the Dynamic Stochastic General Equilibrium Model
, pp. 287 - 300
Publisher: Cambridge University Press
Print publication year: 2006

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